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by com.allthepossibles
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Email subject line check

check_email_subject
Read-only

Check an email subject line. Pass subject. Reports length in characters (phones show roughly the first 30 to 45; desktop 55 to 70, estimates), word count, spam-trigger words from a short list of about 35 phrases (filters weigh many signals, so a hit is a nudge, not a verdict), ALL CAPS, repeated punctuation, dollar signs and percent claims, emoji count, fake Re:/Fwd:, leading or trailing spaces, and personalisation placeholders (found, missing fallback, unbalanced braces, empty tags, leftover undefined/null/NaN).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "subject": "<subject>"
    },
    "name": "check_email_subject"
  }
}

Input Schema

NameRequiredDescriptionDefault
subjectYesThe subject line

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only declare it as a safe read operation, while the description adds substantial behavioral context: all reported checks, mobile/desktop length estimates, and the caveat that spam-trigger hits are a nudge rather than a verdict. Since there is no output schema, this description carries the burden well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the action, and though the second sentence is a dense enumeration, every listed item is substantive rather than filler. It is slightly list-heavy but not bloated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description does a good job enumerating the metrics returned and includes an interpretative caveat about spam signals. It stops short of specifying the exact response structure or how to read non-spam flags, leaving a minor gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is one parameter with 100% schema coverage and the schema already describes it as 'The subject line'. The description adds 'Pass `subject`' but no additional format, length, or encoding constraints, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource (check an email subject line) and enumerates the exact checks it performs, making it distinct from adjacent email checks such as preheader or accessibility. An agent can identify the tool's role without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by 'Check an email subject line. Pass `subject`', but there is no explicit when-to-use or when-not guidance. It does not route the agent to or away from related email-check tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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